EOS Science Poster Series: ICE- Global Ice and Snow

This poster, one in a four-part series, highlights recent images from select NASA Earth Science spacecraft and showcases related research results. The back gives a brief overview of the science and missions behind NASA's study of ICE. Educational levels: Middle school, High school, Undergraduate lower division, Undergraduate upper division, Graduate or professional

Etude de la précision du satellite lidar GLAS-ICESat pour l'altimétrie des eaux continentales

For the coming century, the control of water resources will be certainly the key of all the stakes for billions of human beings. Unfortunately a reduction in the number of stations is observed with a decline of measurements quality. Remote sensing, which saw the development of numerous satellite radar altimeters and more recently the launch of the satellite lidar ICESat, could be an interesting alternative for the study of the hydrological networks. The objective of this study is to estimate the potential of ICESat for monitoring continental water missions through the cases of the Lake Geneva (Switzerland and France) and rivers of Metropolitan France. Our first axis of study concerned the satellite-based assessment of ICESat on the Lake Geneva by comparing laser data to hydrological gauge water levels. Two hydrological stations (Chillon and Saint Prex) were used to evaluate the accuracy of ICESat elevations. First it was necessary that all data was in the same datum to conduct a consistent comparison. ICESat elevations, which are referenced in the Topex ellipsoid, were converted into orthometric elevations by a translation between Topex ellipsoid and WGS84 and then into the vertical reference IGN69 (RGF93) with the grid RAF98. The shots of water alone were then extracted track by track and the mean elevation calculated for each track was used for the comparison with reference elevations (hydrological gauges). The error RMS is 33 cm (-0.20 cm ± 0.21 cm) without any saturation correction. When the saturation correction is supplied and different from -999.000, the quality of water elevation data is improved : the error RMS is 14 cm (0.01 cm ± 0.10 cm). However GLAS temporal profiles show a slow progressive adaptation of GLAS sensor before proposing correct elevations. On the passage of ICESat from the land to water, the first spots elevations are higher than reference elevation and the following spots from 30 cm to 50 cm. The progressive return to the normal can last 0.2 s. It corresponds to 8 measurements and an adaptation distance of 1.360 km. When the transition footprints are excluded, the accuracy for the ICESat elevation measurements is 5 cm. Besides hydrological objects with a small size (small lakes, small rivers), which can not apply a margin of 1.5 km to remove transition footprints, could not be monitoring using ICESat with a good accuracy. Next the accuracy of ICESat was investigated on French rivers with a width larger than the size of ICESat footprint (about 55 m for the laser 3). The error RMS is 1.15 m (0.03 m ± 1.17 m) due to the time of ICESat adaptation on the passage from land to water. ICESat is not adapted for the monitoring of the continental water resource

Forest height maps obtained with ICESAT-2 data

The purpose of this project is to get data from the ICESAT 2 satellite to obtain maps for the regions of Europe and North-America, of the height of the vegetation. To do it, we will use tools as MATLAB to store all this data obtained and compare it through 2d and 3d graphics.El propósito de este proyecto es obtener datos del satélite ICESAT 2 para obtener mapas de la regiones de Europa y Norte América, de la altura de la vegetación. Para ello utilizaremos herramientas como MATLAB para almacenar todos estos datos obtenidos y compararlos mediante gráficos 2d y 3d.El propòsit d'aquest projecte és obtenir dades del satèl·lit ICESAT 2 per obtenir mapes de les regions d'Europa i nord America, de l'alçada de la vegetació. Per això utilitzarem eines com MATLAB per emmagatzemar totes aquestes dades obtingudes i comparar-les mitjançant gràfics 2d i 3d

Estimating Canopy Gap Fraction Using ICESat GLAS within Australian Forest Ecosystems

Spaceborne laser altimetry waveform estimates of canopy Gap Fraction (GF) vary withrespect to discrete return airborne equivalents due to their greater sensitivity to reflectance differencesbetween canopy and ground surfaces resulting from differences in footprint size, energy thresholding,noise characteristics and sampling geometry. Applying scaling factors to either the ground or canopyportions of waveforms has successfully circumvented this issue, but not at large scales. This studydevelops a method to scale spaceborne altimeter waveforms by identifying which remotely-sensedvegetation, terrain and environmental attributes are best suited to predicting scaling factors basedon an independent measure of importance. The most important attributes were identified as: soilphosphorus and nitrogen contents, vegetation height, MODIS vegetation continuous fields productand terrain slope. Unscaled and scaled estimates of GF are compared to corresponding ALS datafor all available data and an optimized subset, where the latter produced most encouraging results(R2 = 0.89, RMSE = 0.10). This methodology shows potential for successfully refining estimates ofGF at large scales and identifies the most suitable attributes for deriving appropriate scaling factors.Large-scale active sensor estimates of GF can establish a baseline from which future monitoringinvestigations can be initiated via upcoming Earth Observation missions

Measuring Individual Tree Height and Crown Diameter for Mangrove Trees with Airborne Lidar Data

Mangroves are unique ecosystems that provide valuable coastal area habitats, protection, and services. Access to observing mangrove forests is typically difficult on the ground. Therefore, it is of interest to develop and evaluate remote sensing methods that enable us to obtain accurate information on the structure of mangrove forests and to monitor their condition in time. The main objective of this study was to develop a methodology for processing airborne lidar data for measuring height and crown diameter for mangrove forests in the north-eastern coastal areas of Brazil. Specific objectives were to: (1) evaluate the most appropriate lidar data processing approach, such as area-based or individual tree methods, (2) investigate the most appropriate parameters for lidar-derived data products when estimating height and crown diameter, such as the spatial resolution of canopy height models and ground elevation models; and (3) compare the accuracy of lidar estimates to field measurements of height and crown diameter. The lidar dataset was acquired over mangrove forest of the northeast of Brazil. The crown diameter was calculated as the average of two values measured along two perpendicular directions from the location of each tree top by fitting a fourth-degree polynomial on both profiles. The lidar-derived tree measurements were used with regression models and cross-validation to estimate plot level field-measured crown diameter. Root mean square error, linear regression and the Nash-Sutcliffe coefficient were also used to compare lidar height and field height. The mean of lidar-estimated tree height was 9,48m and the mean of field tree height was 8.44m. The correlation between lidar tree height and field tree height was r= 0.60, E=-0.06 and RMSE= 2.8. The correlation between height and crown diameter needed to parameterized the individual tree identification software obtained for 32 trees was r= 0.83 and determination coefficient was r2 = 0.69. The results of the current study show that lidar data could be used to estimate height and average crown diameter of mangrove trees and to improve estimates of other mangrove forest biophysical parameters of interest by focusing at the individual tree level. The research presented in this study contributes to the overall knowledge of using lidar remote sensing to measure and monitor mangrove forests

Height and Biomass of Mangroves in Africa from ICEsat/GLAS and SRTM

The accurate quantification of forest 3-D structure is of great importance for studies of the global carbon cycle and biodiversity. These studies are especially relevant in Africa, where deforestation rates are high and the lack of background data is great. Mangrove forests are ecologically significant and it is important to measure mangrove canopy heights and biomass. The objectives of this study are to estimate: 1. The total area, 2. Canopy height distributions and 3. Aboveground biomass of mangrove forests in Africa. To derive mangrove 3-D structure and biomass maps, we used a combination of mangrove maps derived from Landsat ETM+, LiDAR canopy height estimates from ICEsat/GLAS (Ice, Cloud, and land Elevation Satellite/Geoscience Laser Altimeter System) and elevation data from SRTM (Shuttle Radar Topography Mission) for the African continent. More specifically, we extracted mangrove forest areas on the SRTM DEM using Landsat based landcover maps. The LiDAR (Light Detection and Ranging) measurements from the large footprint GLAS sensor were used to derive local estimates of canopy height and calibrate the Interferometric Synthetic Aperture Radar (InSAR) data from SRTM. We then applied allometric equations relating canopy height to biomass in order to estimate above ground biomass (AGB) from the canopy height product. The total mangrove area of Africa was estimated to be 25 960 square kilometers with 83% accuracy. The largest mangrove areas and greatest total biomass was 29 found in Nigeria covering 8 573 km2 with 132 x10(exp 6) Mg AGB. Canopy height across Africa was estimated with an overall root mean square error of 3.55 m. This error also includes the impact of using sensors with different resolutions and geolocation error which make comparison between measurements sensitive to canopy heterogeneities. This study provides the first systematic estimates of mangrove area, height and biomass in Africa. Our results showed that the combination of ICEsat/GLAS and SRTM data is well suited for vegetation 3-D mapping on a continental scale